@article {17754,
	title = {SPOT Databases: Efficient Consistency Checking and Optimistic Selection in Probabilistic Spatial Databases},
	journal = {Knowledge and Data Engineering, IEEE Transactions on},
	volume = {21},
	year = {2009},
	month = {2009/01//},
	pages = {92 - 107},
	abstract = {Spatial probabilistic temporal (SPOT) databases are a paradigm for reasoning with probabilistic statements about where a vehicle may be now or in the future. They express statements of the form "Object O is in spatial region R at some time t with some probability in the interval [L,U]." Past work on SPOT databases has developed selection operators based on selecting SPOT atoms that are entailed by the SPOT database-we call this "cautious" selection. In this paper, we study several problems. First, we note that the runtime of consistency checking and cautious selection algorithms in past work is influenced greatly by the granularity of the underlying Cartesian space. In this paper, we first introduce the notion of "optimistic" selection, where we are interested in returning all SPOT atoms in a database that are consistent with respect to a query, rather than having an entailment relationship. We then develop an approach to scaling SPOT databases that has three main contributions: 1) We develop methods to eliminate variables from the linear programs used in past work, thus greatly reducing the size of the linear programs used-the resulting advances apply to consistency checking, optimistic selection, and cautious selection. 2) We develop a host of theorems to show how we can prune the search space when we are interested in optimistic selection. 3) We use the above contributions to build an efficient index to execute optimistic selection queries over SPOT databases. Our approach is superior to past work in two major respects: First, it makes fewer assumptions than all past works on this topic except that in. Second, our experiments, which are based on real-world data about ship movements, show that our algorithms are much more efficient than those in.},
	keywords = {Cartesian, checking;database, database;data, databases;, databases;visual, indexing;inference, indexing;linear, integrity;database, mechanisms;linear, probabilistic, problem;spatial, problems;temporal, processing;search, program;optimistic, programming;probability;query, query;probabilistic, reasoning;search, selection, selection;consistency, space;cautious, temporal},
	isbn = {1041-4347},
	doi = {10.1109/TKDE.2008.93},
	author = {Parker,A. and Infantes,G. and Grant,J. and V.S. Subrahmanian}
}
@article {12549,
	title = {A Constrained Probabilistic Petri Net Framework for Human Activity Detection in Video},
	journal = {Multimedia, IEEE Transactions on},
	volume = {10},
	year = {2008},
	month = {2008/10//},
	pages = {982 - 996},
	abstract = {Recognition of human activities in restricted settings such as airports, parking lots and banks is of significant interest in security and automated surveillance systems. In such settings, data is usually in the form of surveillance videos with wide variation in quality and granularity. Interpretation and identification of human activities requires an activity model that a) is rich enough to handle complex multi-agent interactions, b) is robust to uncertainty in low-level processing and c) can handle ambiguities in the unfolding of activities. We present a computational framework for human activity representation based on Petri nets. We propose an extension-Probabilistic Petri Nets (PPN)-and show how this model is well suited to address each of the above requirements in a wide variety of settings. We then focus on answering two types of questions: (i) what are the minimal sub-videos in which a given activity is identified with a probability above a certain threshold and (ii) for a given video, which activity from a given set occurred with the highest probability? We provide the PPN-MPS algorithm for the first problem, as well as two different algorithms (naive PPN-MPA and PPN-MPA) to solve the second. Our experimental results on a dataset consisting of bank surveillance videos and an unconstrained TSA tarmac surveillance dataset show that our algorithms are both fast and provide high quality results.},
	keywords = {activity, dataset;automated, detection;human, interactions;security, net;human, nets;image, Petri, probabilistic, processing;multiagent, processing;video, representation;low-level, representation;video, signal, Surveillance, surveillance;, systems;constrained, systems;surveillance, tarmac, TSA, videos;Petri},
	isbn = {1520-9210},
	doi = {10.1109/TMM.2008.2001369},
	author = {Albanese, M. and Chellapa, Rama and Moscato, V. and Picariello, A. and V.S. Subrahmanian and Turaga,P. and Udrea,O.}
}
@conference {17789,
	title = {Approximation results for probabilistic survivability},
	booktitle = {Multi-Agent Security and Survivability, 2005 IEEE 2nd Symposium on},
	year = {2005},
	month = {2005/08//},
	pages = {1 - 10},
	abstract = {As multiagent systems (MASs) are increasingly used in industrial applications, the need to make them more robust and resilient against disruption increases dramatically. The author has developed a probabilistic model (assuming complete ignorance of dependencies between node failures) of survivability based on deploying each agent in a MAS on one or more nodes. Finding a deployment that maximizes survivability is highly intractable for two reasons: firstly, computing the survivability of any deployment is intractable, and secondly, going through an exponential number of deployments to find the best one adds another layer of intractability. In this paper, we study what happens when node failures are independent. We show that computing survivability in this environment is still intractable. We propose various heuristics to compute the survivability of a given deployment. We have implemented and tested all these heuristics. We report on the advantages and disadvantages of different heuristics in different environmental settings.},
	keywords = {application;, computing;, failure;, fault, heuristic, heuristics, industrial, model;, multi-agent, multiagent, node, probabilistic, probability;, programming;, survivability;, system;, systems;, testing;, tolerant},
	doi = {10.1109/MASSUR.2005.1507042},
	author = {Zhang,Y. and Manister,E. and Kraus,S. and V.S. Subrahmanian}
}
@conference {12700,
	title = {Intra-personal kernel space for face recognition},
	booktitle = {Automatic Face and Gesture Recognition, 2004. Proceedings. Sixth IEEE International Conference on},
	year = {2004},
	month = {2004/05//},
	pages = {235 - 240},
	abstract = {Intra-personal space modeling proposed by Moghaddam et al. has been successfully applied in face recognition. In their work the regular principal subspaces are derived from the intra-personal spacce using a principal componen analysis and embedded in a probabilistic formulation. In this paper, we derive the principal subspace from the intro-personal kernel space by developing a probabilistic analysis for kernel principal components for face recognition. We test this algorithm on a subset of the FERET database with illumination and facial expression variations. The recognition performance demonstrates its advantage over other traditional subspace approaches.},
	keywords = {analysis;, component, Expression, Face, facial, illumination, intra-personal, Kernel, lighting;, principal, probabilistic, probability;, recognition;, space;, variation;},
	doi = {10.1109/AFGR.2004.1301537},
	author = {Zhou,Shaohua and Chellapa, Rama and Moghaddam, B.}
}
@conference {17802,
	title = {The priority curve algorithm for video summarization},
	booktitle = {Proceedings of the 2nd ACM international workshop on Multimedia databases},
	series = {MMDB {\textquoteright}04},
	year = {2004},
	month = {2004///},
	pages = {28 - 35},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	abstract = {In this paper, we introduce the concept of a priority curve associated with a video. We then provide an algorithm that can use the priority curve to create a summary (of a desired length) of any video. The summary thus created exhibits nice continuity properties and also avoids repetition. We have implemented the priority curve algorithm (PCA) and compared it with other summarization algorithms in the literature. We show that PCA is faster than existing algorithms and also produces better quality summaries. The quality of summaries was evaluated by a group of 200 students in Naples, Italy, who watched soccer videos. We also briefly describe a soccer video summarization system we have built on using the PCA architecture and various (classical) image processing algorithms.},
	keywords = {probabilistic, Summarization, system, video},
	isbn = {1-58113-975-6},
	doi = {10.1145/1032604.1032611},
	url = {http://doi.acm.org/10.1145/1032604.1032611},
	author = {Fayzullin,M. and V.S. Subrahmanian and Albanese, M. and Picariello, A.}
}
@conference {12695,
	title = {Probabilistic identity characterization for face recognition},
	booktitle = {Computer Vision and Pattern Recognition, 2004. CVPR 2004. Proceedings of the 2004 IEEE Computer Society Conference on},
	volume = {2},
	year = {2004},
	month = {2004/07/02/june},
	pages = {II-805 - II-812 Vol.2 - II-805 - II-812 Vol.2},
	abstract = {We present a general framework for characterizing the object identity in a single image or a group of images with each image containing a transformed version of the object, with applications to face recognition. In terms of the transformation, the group is made of either many still images or frames of a video sequence. The object identity is either discrete- or continuous-valued. This probabilistic framework integrates all the evidence of the set and handles the localization problem, illumination and pose variations through subspace identity encoding. Issues and challenges arising in this framework are addressed and efficient computational schemes are presented. Good face recognition results using the PIE database are reported.},
	keywords = {characterization;, database, database;, encoding;, Face, identity, identity;, image, localization, management, object, PIE, probabilistic, problem;, recognition;, sequence;, sequences;, subspace, systems;, video},
	doi = {10.1109/CVPR.2004.1315247},
	author = {Zhou,S. K and Chellapa, Rama}
}
@conference {12734,
	title = {Combining multiple evidences for gait recognition},
	booktitle = {Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP {\textquoteright}03). 2003 IEEE International Conference on},
	volume = {3},
	year = {2003},
	month = {2003/04//},
	pages = {III - 33-6 vol.3 - III - 33-6 vol.3},
	abstract = {In this paper, we systematically analyze different components of human gait, for the purpose of human identification. We investigate dynamic features such as the swing of the hands/legs, the sway of the upper body and static features like height, in both frontal and side views. Both probabilistic and non-probabilistic techniques are used for matching the features. Various combination strategies may be used depending upon the gait features being combined. We discuss three simple rules: the Sum, Product and MIN rules that are relevant to our feature sets. Experiments using four different datasets demonstrate that fusion can be used as an effective strategy in recognition.},
	keywords = {analysis;, dynamic, evidences;, extraction;, feature, features;, frontal, Gait, height;, human, identification;, image, MIN, multiple, nonprobabilistic, probabilistic, Product, recognition;, rules;, sets;, side, static, Sum, sway;, swing;, techniques;, views;},
	doi = {10.1109/ICASSP.2003.1199100},
	author = {Cuntoor, N. and Kale, A. and Chellapa, Rama}
}
@conference {13240,
	title = {Exemplar-based tracking and recognition of arm gestures},
	booktitle = {Image and Signal Processing and Analysis, 2003. ISPA 2003. Proceedings of the 3rd International Symposium on},
	volume = {2},
	year = {2003},
	month = {2003/09//},
	pages = {656 - 661 Vol.2 - 656 - 661 Vol.2},
	abstract = {This paper presents a probabilistic exemplar-based framework for recognizing gestures. The approach is based on representing each gesture as a sequence of learned body poses. The gestures are recognized through a probabilistic framework for matching these body poses and for imposing temporal constrains between different poses. Matching individual poses to image data is performed using a probabilistic formulation for edge matching to obtain a likelihood measurement for each individual pose. The paper introduces a correspondence-free weighted matching scheme for edge templates that emphasize discriminating features in the matching. The weighting does not require establishing correspondences between the different pose models. The probabilistic framework also imposes temporal constrains between different pose through a learned hidden Markov model (HMM) of each gesture.},
	keywords = {arm, constrains;, correspondence-free, edge, exemplar-based, framework;, gesture, hidden, HMM;, image, logic;, Markov, MATCHING, matching;, model;, models;, probabilistic, recognition;, scheme;, segmentation;, temporal, tracking;, weighted},
	doi = {10.1109/ISPA.2003.1296358},
	author = {Elgammal,A. and Shet,V. and Yacoob,Yaser and Davis, Larry S.}
}
@conference {13247,
	title = {Learning dynamics for exemplar-based gesture recognition},
	booktitle = {Computer Vision and Pattern Recognition, 2003. Proceedings. 2003 IEEE Computer Society Conference on},
	volume = {1},
	year = {2003},
	month = {2003/06//},
	pages = {I-571 - I-578 vol.1 - I-571 - I-578 vol.1},
	abstract = {This paper addresses the problem of capturing the dynamics for exemplar-based recognition systems. Traditional HMM provides a probabilistic tool to capture system dynamics and in exemplar paradigm, HMM states are typically coupled with the exemplars. Alternatively, we propose a non-parametric HMM approach that uses a discrete HMM with arbitrary states (decoupled from exemplars) to capture the dynamics over a large exemplar space where a nonparametric estimation approach is used to model the exemplar distribution. This reduces the need for lengthy and non-optimal training of the HMM observation model. We used the proposed approach for view-based recognition of gestures. The approach is based on representing each gesture as a sequence of learned body poses (exemplars). The gestures are recognized through a probabilistic framework for matching these body poses and for imposing temporal constraints between different poses using the proposed non-parametric HMM.},
	keywords = {arbitrary, body, by, Computer, constraint;, detection;, discrete, distribution, dynamics;, edge, estimation;, example;, exemplar, exemplar-based, extraction;, feature, framework;, gesture, gesture;, hidden, HMM;, human, image, learning, Markov, matching;, model;, models;, motion;, nonparametric, pose, probabilistic, recognition;, sequence;, space;, state;, statistics;, system, temporal, tool;, view-based, vision;},
	doi = {10.1109/CVPR.2003.1211405},
	author = {Elgammal,A. and Shet,V. and Yacoob,Yaser and Davis, Larry S.}
}
@conference {13248,
	title = {Probabilistic tracking in joint feature-spatial spaces},
	booktitle = {Computer Vision and Pattern Recognition, 2003. Proceedings. 2003 IEEE Computer Society Conference on},
	volume = {1},
	year = {2003},
	month = {2003/06//},
	pages = {I-781 - I-788 vol.1 - I-781 - I-788 vol.1},
	abstract = {In this paper, we present a probabilistic framework for tracking regions based on their appearance. We exploit the feature-spatial distribution of a region representing an object as a probabilistic constraint to track that region over time. The tracking is achieved by maximizing a similarity-based objective function over transformation space given a nonparametric representation of the joint feature-spatial distribution. Such a representation imposes a probabilistic constraint on the region feature distribution coupled with the region structure, which yields an appearance tracker that is robust to small local deformations and partial occlusion. We present the approach for the general form of joint feature-spatial distributions and apply it to tracking with different types of image features including row intensity, color and image gradient.},
	keywords = {analysis;, appearance, appearance;, color;, colour, Computer, constraint;, deformation;, detection;, distribution;, edge, estimation;, extraction;, feature, feature-spatial, feature;, function, gradient;, image, intensity;, joint, likelihood, local, maximization;, maximum, nonparametric, object, objective, occlusion;, optical, partial, probabilistic, probability;, region, representation;, row, similarity-based, small, space;, structure;, target, tracker;, tracking;, transformation, vision;},
	doi = {10.1109/CVPR.2003.1211432},
	author = {Elgammal,A. and Duraiswami, Ramani and Davis, Larry S.}
}
@conference {14387,
	title = {PXML: a probabilistic semistructured data model and algebra},
	booktitle = {Data Engineering, 2003. Proceedings. 19th International Conference on},
	year = {2003},
	month = {2003/03//},
	pages = {467 - 478},
	abstract = {Despite the recent proliferation of work on semistructured data models, there has been little work to date on supporting uncertainty in these models. We propose a model for probabilistic semistructured data (PSD). The advantage of our approach is that it supports a flexible representation that allows the specification of a wide class of distributions over semistructured instances. We provide two semantics for the model and show that the semantics are probabilistically coherent. Next, we develop an extension of the relational algebra to handle probabilistic semistructured data and describe efficient algorithms for answering queries that use this algebra. Finally, we present experimental results showing the efficiency of our algorithms.},
	keywords = {algebra;, data, databases;, instances;, model;, models;, probabilistic, processing;, PXML;, query, relational, semistructured, structures;, tree, XML;},
	doi = {10.1109/ICDE.2003.1260814},
	author = {Hung,E. and Getoor, Lise and V.S. Subrahmanian}
}
@article {17809,
	title = {Temporal probabilistic object bases},
	journal = {Knowledge and Data Engineering, IEEE Transactions on},
	volume = {15},
	year = {2003},
	month = {2003/08//july},
	pages = {921 - 939},
	abstract = {There are numerous applications where we have to deal with temporal uncertainty associated with objects. The ability to automatically store and manipulate time, probabilities, and objects is important. We propose a data model and algebra for temporal probabilistic object bases (TPOBs), which allows us to specify the probability with which an event occurs at a given time point. In explicit TPOB-instances, the sets of time points along with their probability intervals are explicitly enumerated. In implicit TPOB-instances, sets of time points are expressed by constraints and their probability intervals by probability distribution functions. Thus, implicit object base instances are succinct representations of explicit ones; they allow for an efficient implementation of algebraic operations, while their explicit counterparts make defining algebraic operations easy. We extend the relational algebra to both explicit and implicit instances and prove that the operations on implicit instances correctly implement their counterpart on explicit instances.},
	keywords = {algebra;, algebraic, bases;, constraints;, data, database, database;, databases;, distribution, explicit, functions;, handling;, implicit, instances;, integrity;, intervals;, management;, model;, models;, object, object-oriented, operations;, probabilistic, probability, probability;, relational, temporal, theory;, Uncertainty, uncertainty;},
	isbn = {1041-4347},
	doi = {10.1109/TKDE.2003.1209009},
	author = {Biazzo,V. and Giugno,R. and Lukasiewicz,T. and V.S. Subrahmanian}
}
@conference {12766,
	title = {Probabilistic recognition of human faces from video},
	booktitle = {Image Processing. 2002. Proceedings. 2002 International Conference on},
	volume = {1},
	year = {2002},
	month = {2002///},
	pages = {I-41 - I-44 vol.1 - I-41 - I-44 vol.1},
	abstract = {Most present face recognition approaches recognize faces based on still images. We present a novel approach to recognize faces in video. In that scenario, the face gallery may consist of still images or may be derived from a videos. For evidence integration we use classical Bayesian propagation over time and compute the posterior distribution using sequential importance sampling. The probabilistic approach allows us to handle uncertainties in a systematic manner. Experimental results using videos collected by NIST/USF and CMU illustrate the effectiveness of this approach in both still-to-video and video-to-video scenarios with appropriate model choices.},
	keywords = {Bayes, Bayesian, CMU;, distribution;, Face, faces;, gallery;, handling;, human, image, images;, importance, likelihood;, methods;, NIST/USF;, observation, posterior, probabilistic, probability;, processing;, propagation;, recognition;, sampling;, sequential, signal, still, Still-to-video, Uncertainty, video, Video-to-video},
	doi = {10.1109/ICIP.2002.1037954},
	author = {Chellapa, Rama and Kruger, V. and Zhou,Shaohua}
}
@conference {12756,
	title = {A robust algorithm for probabilistic human recognition from video},
	booktitle = {Pattern Recognition, 2002. Proceedings. 16th International Conference on},
	volume = {1},
	year = {2002},
	month = {2002///},
	pages = {226 - 229 vol.1 - 226 - 229 vol.1},
	abstract = {Human recognition from video requires solving the two tasks, recognition and tracking, simultaneously. This leads to a parameterized time series state space model, representing both motion and identity of the human. Sequential Monte Carlo (SMC) algorithms, like Condensation, can be developed to offer numerical solutions to this model. However in outdoor environments, the solution is more likely to diverge from the foreground, causing failures in both recognition and tracking. In this paper we propose an approach for tackling this problem by incorporating the constraint of temporal continuity in the observations. Experimental results demonstrate improvements over its Condensation counterpart.},
	keywords = {algorithm;, algorithms;, Carlo, continuity;, human, image, methods;, model;, Monte, parameterized, probabilistic, recognition;, robust, sequential, series, series;, space, state, state-space, temporal, TIME},
	doi = {10.1109/ICPR.2002.1044661},
	author = {Zhou,Shaohua and Chellapa, Rama}
}
